{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JmF5ohLbPlF"
      },
      "source": [
        "# Pre processing steps of a COCO JSON annotated file"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXwwz3PlbUX2"
      },
      "source": [
        "Given a single COCO annotated JSON file, your goal is to pre-process in order to remove noise and manipulate it into a form which is suitable for training a ML model. This script will also check if the annotated images are broken or missing.The output of this notebook should be 2 JSON annotation files -\n",
        "`Material and its sub type (e.g Plastics_HDPE)` and `'Material form and its sub type (e.g.Paper-Products-White-Paper)'`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1SxGZD2bv8E"
      },
      "source": [
        "The COCO annotation file includes the following -\n",
        "\n",
        "1. Name of the images.\n",
        "\n",
        "2. Dimensions of the images.\n",
        "\n",
        "3. Classes in the image category.\n",
        "\n",
        "4. Name of the super categories of the classes.\n",
        "\n",
        "5. Area acquired by the segmented pixels in an image.\n",
        "\n",
        "6. Bounding box co-ordinates.\n",
        "\n",
        "7. Annotated segmentation coordinates."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j0v31gxTbweO"
      },
      "source": [
        "There is a lot of noise in the real world annotation file. The images name could be wrong. The images mentioned in an annotation file may not be present in the image folder, which will disrupt the model training procedure. The contents within an annotation file may not match with each other. Even the files present in an image folder may be broken or truncated, which will cause errors while reading image files. Our goal is to eradicate all these problems."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PyFn96EKb7A-"
      },
      "source": [
        "Our goal is to make sure that all information in the key values corresponds to each other correctly. This notebook will help you achieve this task."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6aXxxox0DDa"
      },
      "source": [
        "## Import labels and sample JSON file\n",
        "To import total classes for the material, material_form and plastic_type we will import the label files from the waste_identification_ml project from Tensorflow Model Garden.\n",
        "We will also import a noisy sample JSON file to illustrate an example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WluEHMZYm0zM",
        "outputId": "7bdc802d-4b11-4959-8255-64099ff530a4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  3862  100  3862    0     0  18499      0 --:--:-- --:--:-- --:--:-- 18567\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  2427  100  2427    0     0   8695      0 --:--:-- --:--:-- --:--:--  8667\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100   264  100   264    0     0    935      0 --:--:-- --:--:-- --:--:--   936\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100   422  100   422    0     0   1525      0 --:--:-- --:--:-- --:--:--  1528\n",
            "mkdir: cannot create directory ‘image_folder’: File exists\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100 3303k  100 3303k    0     0  4518k      0 --:--:-- --:--:-- --:--:-- 4518k\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "curl -O \"https://raw.githubusercontent.com/tensorflow/models/master/official/\"\\\n",
        "\"projects/waste_identification_ml/two_model_inference/labels.py\"\n",
        "\n",
        "curl -O \"https://raw.githubusercontent.com/tensorflow/models/master/official/\"\\\n",
        "\"projects/waste_identification_ml/pre_processing/config/sample_json/dataset.json\"\n",
        "\n",
        "\n",
        "curl -O \"https://raw.githubusercontent.com/tensorflow/models/master/official/\"\\\n",
        "\"projects/waste_identification_ml/pre_processing/config/data/\"\\\n",
        "\"two_model_strategy_material.csv\"\n",
        "\n",
        "curl -O \"https://raw.githubusercontent.com/tensorflow/models/master/official/\"\\\n",
        "\"projects/waste_identification_ml/pre_processing/config/data/\"\\\n",
        "\"two_model_strategy_material_form.csv\"\n",
        "\n",
        "mkdir image_folder\n",
        "\n",
        "curl -o image_folder/image_2.png \"https://raw.githubusercontent.com/\"\\\n",
        "\"tensorflow/models/master/official/projects/waste_identification_ml/\"\\\n",
        "\"pre_processing/config/sample_images/image_2.png\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRhCAFlVcRm0"
      },
      "source": [
        "## Import the required libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mnxbo8GBcN2O"
      },
      "outputs": [],
      "source": [
        "import glob\n",
        "import tqdm\n",
        "import json\n",
        "from PIL import Image\n",
        "import subprocess\n",
        "import copy\n",
        "import os\n",
        "from google.colab import files\n",
        "from labels import load_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tGOCdeiucUgq"
      },
      "outputs": [],
      "source": [
        "# @title Utility Functions { display-mode: \"form\", run: \"auto\" }\n",
        "def read_json(file):\n",
        "  \"\"\"Read any JSON file.\n",
        "\n",
        "  Args:\n",
        "    file: path to the file\n",
        "  \"\"\"\n",
        "  with open(file) as json_file:\n",
        "    data = json.load(json_file)\n",
        "  return data\n",
        "\n",
        "\n",
        "def search_dict_value(dic, id):\n",
        "  \"\"\"Returns the key of the dictionary from its value'\n",
        "\n",
        "  Args:\n",
        "    dic = Mapping to search by value.\n",
        "    id = Value to search.\n",
        "  \"\"\"\n",
        "  key_list = list(dic.keys())\n",
        "  val_list = list(dic.values())\n",
        "  position = val_list.index(id)\n",
        "  return key_list[position]\n",
        "\n",
        "\n",
        "def delete_truncated_images(folder_path: str) -\u003e None:\n",
        "  \"\"\"Find and delete truncated images.\n",
        "\n",
        "  Args:\n",
        "    folder_path: path to the folder where images are saved.\n",
        "  \"\"\"\n",
        "  # path to the images folder to read its content\n",
        "  files = glob.glob(folder_path + '/*')\n",
        "  print('Total number of files in the folder:', len(files))\n",
        "\n",
        "  num = 0\n",
        "\n",
        "  # read all image files and remove them from the directory in case they are broken\n",
        "  for file in tqdm.tqdm(files):\n",
        "    if file.endswith(('.png','.jpg')):\n",
        "      try:\n",
        "        img = Image.open(file)\n",
        "        img.verify()\n",
        "      except:\n",
        "        num = num + 1\n",
        "        subprocess.run(['rm', file])\n",
        "        print('Broken file name:  ' + file)\n",
        "  if num == 0:\n",
        "    print('\\nNo broken images found')\n",
        "  else:\n",
        "    print('Total number of broken images found:', num)\n",
        "\n",
        "\n",
        "def spelling_correction(dic, typo_dict):\n",
        "  \"\"\"Correcting some common spelling mistakes.\"\"\"\n",
        "  for i in dic['categories']:\n",
        "    for old, new in typo_dict.items():\n",
        "      i['name'] = i['name'].replace(old, new)\n",
        "  return dic\n",
        "\n",
        "\n",
        "def labeling_correction(dic, labels_dict, num):\n",
        "  \"\"\"Matching annotated labels with the correct labels.\n",
        "\n",
        "  Mapping the modified labeling ID with the corresponding original ID for alignment\n",
        "  of categories.\n",
        "\n",
        "  Args:\n",
        "    dic: JSON file read as a dictionary\n",
        "    num: keyword position inside the label\n",
        "    labels_dict: dictionary showing the labels ID of the original categories\n",
        "  \"\"\"\n",
        "  mapping_list = []\n",
        "  incorrect_labels = []\n",
        "\n",
        "  for i in dic['categories']:\n",
        "      sp = i['name'].split('_')\n",
        "\n",
        "      if num == 1:\n",
        "          target_value = sp[0].lower() + '_' + sp[1].lower()\n",
        "      elif num == 4:\n",
        "          target_value = sp[4].lower()\n",
        "      else:\n",
        "          raise ValueError(\"Invalid value for 'num'\")\n",
        "\n",
        "      if target_value in labels_dict.values():\n",
        "          id_match = search_dict_value(labels_dict, target_value)\n",
        "          mapping_list.append((i['id'], target_value, id_match))\n",
        "      else:\n",
        "          incorrect_labels.append(i['id'])\n",
        "\n",
        "  return mapping_list, incorrect_labels\n",
        "\n",
        "\n",
        "def images_key(dic):\n",
        "  \"\"\"Align the data within the dictionary in the 'images' key.\n",
        "\n",
        "  The 'image_id' parameter in the 'annotation' key is the same as 'id' in the 'images' key of the dictionary. This function\n",
        "  will also remove all image data from the 'images' key whose 'id' does not\n",
        "  match with 'image_id' in the 'annotation' key in the dictionary.\n",
        "\n",
        "  Args:\n",
        "    dic: where the JSON file is read into\n",
        "  \"\"\"\n",
        "  image_ids = set(i['image_id'] for i in dic['annotations'])\n",
        "  new_images = [i for i in dic['images'] if i['id'] in image_ids]\n",
        "  return new_images\n",
        "\n",
        "\n",
        "def annotations_key(dic, incorrect_labels, mapping_dict):\n",
        "  \"\"\"Align the data within the dictionary  in the 'annotation' key.\n",
        "\n",
        "  Notice that the 'category_id' in the 'annotation' key is same as 'id'\n",
        "  in the 'categories' key of the dictionary.\n",
        "\n",
        "  Args:\n",
        "    dic: where the JSON file is read into\n",
        "  \"\"\"\n",
        "  new_annotation = []\n",
        "\n",
        "  for i in dic['annotations']:\n",
        "    id = i['category_id']\n",
        "    if (id not in incorrect_labels) and (id in [tup[0] for tup in mapping_dict]):\n",
        "      new_id = [i[2] for i in mapping_dict if i[0] == id][0]\n",
        "      i['category_id'] = new_id\n",
        "      new_annotation.append(i)\n",
        "  return new_annotation\n",
        "\n",
        "\n",
        "def annotated_images(folder_path, dic):\n",
        "  \"\"\"Get images infromation that are mentioned in an annotation file but are not present in an image folder.\n",
        "\n",
        "  Args:\n",
        "    folder_path: path of an image folder.\n",
        "  \"\"\"\n",
        "  # read the file names from the directory\n",
        "  files = glob.glob(folder_path + '/*')\n",
        "  files = set(map(os.path.basename, files))\n",
        "\n",
        "  # list of images in an annotation file\n",
        "  dic['images'] = [i for i in dic['images'] if i['file_name'] in files]\n",
        "  return dic\n",
        "\n",
        "\n",
        "def image_annotation_key(dic):\n",
        "  \"\"\"Check if same images are present in both \"images\" key and \"annotations\" key.\n",
        "\n",
        "  List of the image IDs which are in the \"images\" key but NOT in \"annotation\" key.\n",
        "  Remove information if they are not present in both keys.\n",
        "\n",
        "  Args:\n",
        "    dic: annotation file read as a dictionary\n",
        "  \"\"\"\n",
        "  images_id = [i['id'] for i in dic['images']]\n",
        "  annotation_id = [i['image_id'] for i in dic['annotations']]\n",
        "  common_list = set(images_id).intersection(annotation_id)\n",
        "  dic['images'] = [i for i in dic['images'] if i['id'] in common_list]\n",
        "  dic['annotations'] = [i for i in dic['annotations'] if i['image_id'] in common_list]\n",
        "  return dic\n",
        "\n",
        "\n",
        "def categories_dictionary(list_of_objects):\n",
        "  \"\"\"Generates a list of dictionaries representing categories of objects.\n",
        "\n",
        "  Each dictionary has an 'id' corresponding to its order in the list, a 'name'\n",
        "  taken from the input list, and a fixed 'supercategory' set as 'objects'.\n",
        "\n",
        "  Args:\n",
        "      list_of_objects: List of object names to be used as categories.\n",
        "\n",
        "  Returns:\n",
        "      list: List of dictionaries, each representing a category.\n",
        "\n",
        "  Example:\n",
        "      \u003e\u003e\u003e categories_dictionary(['car', 'bus'])\n",
        "      [{'id': 1, 'name': 'car', 'supercategory': 'objects'},\n",
        "        {'id': 2, 'name': 'bus', 'supercategory': 'objects'}]\n",
        "  \"\"\"\n",
        "  objects_dictionaries = []\n",
        "  for num, m in enumerate(list_of_objects, start=1):\n",
        "    objects_dictionaries.append({\n",
        "        'id': num,\n",
        "        'name': m,\n",
        "        'supercategory': 'objects'\n",
        "    })\n",
        "\n",
        "  return objects_dictionaries\n",
        "\n",
        "\n",
        "def print_incorrect_labels(incorrect_labels, data_postprocessing, m):\n",
        "    \"\"\"Prints the incorrect labels and their count.\n",
        "\n",
        "    Args:\n",
        "        incorrect_labels: List of incorrect label IDs.\n",
        "        data_postprocessing: The data containing postprocessing details.\n",
        "        m: A tuple where the element denotes a condition value.\n",
        "    \"\"\"\n",
        "    print('\\nTotal number of incorrect labels:', len(incorrect_labels))\n",
        "    print('Incorrect labels are below: ')\n",
        "\n",
        "    for category in data_postprocessing['categories']:\n",
        "        if category['id'] in incorrect_labels:\n",
        "            name_parts = category['name'].split('_')\n",
        "\n",
        "            if m == 1 and len(name_parts) \u003e= 2:\n",
        "                print(f'{name_parts[0]}_{name_parts[1]}')\n",
        "            elif m == 4 and len(name_parts) \u003e= 5:\n",
        "                print(name_parts[4])\n",
        "    print('')\n",
        "\n",
        "\n",
        "def print_dict_characteristics(stage_num, dic):\n",
        "    \"\"\"Prints characteristics of the dictionary after post processing.\n",
        "\n",
        "    Args:\n",
        "        stage_num: The stage number of post processing.\n",
        "        data_postprocessing: The data containing postprocessing details.\n",
        "    \"\"\"\n",
        "    print(f'Dictionary characteristics after post processing stage {stage_num}:')\n",
        "    print('images:', len(dic['images']),\n",
        "          'categories:', len(dic['categories']),\n",
        "          'annotations:', len(dic['annotations']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LG7OgYECSL2g"
      },
      "outputs": [],
      "source": [
        "LABELS = {\n",
        "'material_model' : 'two_model_strategy_material.csv',\n",
        "'material_form_model' : 'two_model_strategy_material_form.csv',\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XCyyokyRoydN"
      },
      "outputs": [],
      "source": [
        "# common labeling typo errors that have occured in the past data\n",
        "_KNOWN_TYPOS_IN_MATERIAL = {\n",
        "  '_PET_':'_PETE_',\n",
        "  'plastic_PP_':'_Plastics_PP_',\n",
        "  '_nothing_':'_Na_',\n",
        "  'plastic_MLP_':'Plastics_Others-MLP_',\n",
        "  'plastic_LDPE_':'Plastics_LDPE_',\n",
        "  'plastic_HDPE_':'Plastics_HDPE_',\n",
        "  'Metals_Aluminium_':'Metals_Na_',\n",
        "  'Plastics_PETEE_':'Plastics_PET_',\n",
        "  'Plastics_peTE_':'Plastics_PET_',\n",
        "  'Plastics_PETE_':'Plastics_PET_',\n",
        "}\n",
        "\n",
        "_KNOWN_TYPOS_IN_MATERIAL_FORM = {\n",
        "  'and': '\u0026',\n",
        "  '_Cassete_': '_Cassette_',\n",
        "  '_Toy_':'_Toys_',\n",
        "  '_Toyss_':'toys',\n",
        "  '_Cup-and-Glass_':'_Cup-\u0026-glass_',\n",
        "  '_Tanglers_':'_Tangler_',\n",
        "  '_tub_':'_Container_',\n",
        "  '_Jar_':'_Jug-\u0026-Jar_',\n",
        "  '_Mug-\u0026-Tub_':'_Container_',\n",
        "  '_nothing_':'_Na_',\n",
        "  '_Jugs_':'_Jug-\u0026-Jar_',\n",
        "  '_Cans_':'_Can_',\n",
        "  '_Bottlee_':'_Bottle_',\n",
        "  '_Tub_':'_Container_',\n",
        "  '_Flexiblesiii_':'_Flexibles_',\n",
        "  '_Paper-products-Whitepaper':'_Paper-Products-White-Paper_',\n",
        "  '_Paper-products-Other_':'_Paper-Products_',\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f-05VwsL0mCi"
      },
      "outputs": [],
      "source": [
        "# reading labels\n",
        "images_folder_path = 'image_folder/' #@param {type:\"string\"}\n",
        "\n",
        "category_indices, category_index = load_labels(LABELS)\n",
        "\n",
        "list_of_material = category_indices[0]\n",
        "list_of_material.remove('Na')\n",
        "\n",
        "list_of_material_form = category_indices[1]\n",
        "list_of_material_form.remove('Na')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uZb1rvoWXKAr",
        "outputId": "d63cbf50-c3e4-4828-ebe9-242f78bbffd2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Fiber_Na',\n",
              " 'Food_Na',\n",
              " 'Glass_Na',\n",
              " 'Inorganic-wastes_Na',\n",
              " 'Metals_Na',\n",
              " 'Plastics_HDPE',\n",
              " 'Plastics_LDPE',\n",
              " 'Plastics_Others-HIPC',\n",
              " 'Plastics_Others-MLP',\n",
              " 'Plastics_Others-Tetrapak',\n",
              " 'Plastics_PET',\n",
              " 'Plastics_PP',\n",
              " 'Plastics_PS',\n",
              " 'Plastics_PVC',\n",
              " 'Rubber-\u0026-Leather_Na',\n",
              " 'Textiles_Na',\n",
              " 'Wood_Na',\n",
              " 'Yard-trimming_Na']"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# display labels only for 'material' model\n",
        "list_of_material"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xK-ae7HqXcn-",
        "outputId": "350620a2-ae3d-441c-9636-fea73ff6083e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Bag',\n",
              " 'Battery',\n",
              " 'Blister-pack',\n",
              " 'Book-\u0026-magazine',\n",
              " 'Bottle',\n",
              " 'Box',\n",
              " 'Brush',\n",
              " 'Bulb',\n",
              " 'Can',\n",
              " 'Cards',\n",
              " 'Carton',\n",
              " 'Cassette-\u0026-tape',\n",
              " 'Clamshell',\n",
              " 'Clothes',\n",
              " 'Container',\n",
              " 'Cosmetic',\n",
              " 'Cup-\u0026-glass',\n",
              " 'Cutlery',\n",
              " 'Electronic-devices',\n",
              " 'Flexibles',\n",
              " 'Foil',\n",
              " 'Foot-wear',\n",
              " 'Hangers',\n",
              " 'Jug-\u0026-Jar',\n",
              " 'Lid',\n",
              " 'Mirror',\n",
              " 'Office-Stationary',\n",
              " 'Paper-Products-Others',\n",
              " 'Paper-Products-Others-Cardboard',\n",
              " 'Paper-Products-Others-Newspaper',\n",
              " 'Paper-Products-Others-Whitepaper',\n",
              " 'Pipe',\n",
              " 'Sachets-\u0026-Pouch',\n",
              " 'Scissor',\n",
              " 'Tangler',\n",
              " 'Toys',\n",
              " 'Tray',\n",
              " 'Tube']"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# display labels only for 'material_form' model\n",
        "list_of_material_form"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OoDmNC22ycz"
      },
      "source": [
        "## Find and delete truncated images from the image folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bUUu3F6I20w3",
        "outputId": "a1ae3164-3f68-4745-bdee-23210113fd64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total number of files in the folder: 1\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 1/1 [00:00\u003c00:00, 41.28it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "No broken images found\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "delete_truncated_images(images_folder_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65XuyPBSea7-"
      },
      "source": [
        "## Perform operations on the file\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-uMtZK2edPY",
        "outputId": "771215ed-8950-419e-c7e8-380529f6beb8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['images', 'annotations', 'categories'])\n"
          ]
        }
      ],
      "source": [
        "# read json file and it should contain at least the three keys as shown below\n",
        "path_to_json = 'dataset.json' #@param {type:\"string\"}\n",
        "data = read_json(path_to_json)\n",
        "print(data.keys())\n",
        "\n",
        "# create a copy to compare the results in the end\n",
        "data_preprocessing = copy.deepcopy(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8w7MfDtvDIq",
        "outputId": "6a48ea9d-084b-493a-e5ca-80c48fa5cbf7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 6/6 [00:00\u003c00:00, 45590.26it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Total number of wrong annotated labels are 5\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# checking labeling mistakes as all annotated labels should have 6 keywords connected by '_'\n",
        "num = 0\n",
        "for i in tqdm.tqdm(data['categories']):\n",
        "  if len(i['name'].split('_')) != 6:\n",
        "    num += 1\n",
        "print('\\nTotal number of wrong annotated labels are', num)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2jOWegZxPEp",
        "outputId": "e988f4b0-e254-4fbe-80f3-99fe3a3504e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Total number of labels which has less than 6 keywords are 0\n"
          ]
        }
      ],
      "source": [
        "# remove category labels which has less than 6 keywords\n",
        "categories = []\n",
        "num = 0\n",
        "for i in data['categories']:\n",
        "  if len(i['name'].split('_')) \u003e= 6:\n",
        "    categories.append(i)\n",
        "  else:\n",
        "    num += 1\n",
        "print('\\nTotal number of labels which has less than 6 keywords are', num)\n",
        "data['categories'] = categories"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qup_-ReIz-iv",
        "outputId": "13fae63a-e34f-4e1a-f071-475dfacf028f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 6/6 [00:00\u003c00:00, 51463.85it/s]\n"
          ]
        }
      ],
      "source": [
        "# According to the collected data it was found that most issues occurs from the\n",
        "# 6th keyword which are the sub category of the material form.\n",
        "\n",
        "for i in tqdm.tqdm(data['categories']):\n",
        "  l1 = i['name'].split('_')[:5]\n",
        "  l2 = i['name'].split('_')[5:]\n",
        "  l1.append('-'.join(l2))\n",
        "  i['name'] = '_'.join(l1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYqGTRluopxb",
        "outputId": "8fb71664-1bf1-41af-ae05-3b89492a0a44"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 6/6 [00:00\u003c00:00, 43464.29it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Total number of wrong annotated labels are 0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# checking labeling mistakes as all annotated labels should have 6 keywords connected by '_'\n",
        "num = 0\n",
        "for i in tqdm.tqdm(data['categories']):\n",
        "  if len(i['name'].split('_')) != 6:\n",
        "    num += 1\n",
        "print('\\nTotal number of wrong annotated labels are', num)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MfXv6qrTpDA-",
        "outputId": "111cf89b-6be4-4103-926a-9949fb235139"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dictionary characteristics before processing :\n",
            "images: 2 categories: 6 annotations: 6\n"
          ]
        }
      ],
      "source": [
        "print('Dictionary characteristics before processing :')\n",
        "print('images:',len(data_preprocessing['images']),'categories:', len(data_preprocessing['categories']),'annotations:',len(data_preprocessing['annotations']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ro8KNGaGFv7k",
        "outputId": "05c388a4-942d-4620-d502-742e6cb989f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dictionary characteristics after post processing stage 1:\n",
            "images: 2 categories: 6 annotations: 6\n",
            "\n",
            "Total number of incorrect labels: 1\n",
            "Incorrect labels are below: \n",
            "Plastics_na\n",
            "\n",
            "Dictionary characteristics after post processing stage 2:\n",
            "images: 2 categories: 18 annotations: 6\n",
            "Dictionary characteristics after post processing stage 3:\n",
            "images: 2 categories: 18 annotations: 5\n",
            "Dictionary characteristics after post processing stage 4:\n",
            "images: 1 categories: 18 annotations: 5\n",
            "Dictionary characteristics after post processing stage 5:\n",
            "images: 1 categories: 18 annotations: 4\n",
            "\n",
            "Dictionary characteristics after processing of material_type_annotation :\n",
            "images: 1 categories: 18 annotations: 4\n",
            "###################################################################\n",
            "Dictionary characteristics after post processing stage 1:\n",
            "images: 2 categories: 6 annotations: 6\n",
            "\n",
            "Total number of incorrect labels: 0\n",
            "Incorrect labels are below: \n",
            "\n",
            "Dictionary characteristics after post processing stage 2:\n",
            "images: 2 categories: 38 annotations: 6\n",
            "Dictionary characteristics after post processing stage 3:\n",
            "images: 2 categories: 38 annotations: 6\n",
            "Dictionary characteristics after post processing stage 4:\n",
            "images: 1 categories: 38 annotations: 6\n",
            "Dictionary characteristics after post processing stage 5:\n",
            "images: 1 categories: 38 annotations: 5\n",
            "\n",
            "Dictionary characteristics after processing of material_form_type_annotation :\n",
            "images: 1 categories: 38 annotations: 5\n",
            "###################################################################\n"
          ]
        }
      ],
      "source": [
        "list_of_categories = [(list_of_material,1,'material_type_annotation.json',_KNOWN_TYPOS_IN_MATERIAL),\\\n",
        "                      (list_of_material_form,4,'material_form_type_annotation.json',_KNOWN_TYPOS_IN_MATERIAL_FORM)]\n",
        "\n",
        "for m in list_of_categories:\n",
        "\n",
        "  data_processing = copy.deepcopy(data)\n",
        "\n",
        "  objects_dictionaries = categories_dictionary(m[0])\n",
        "\n",
        "  # create a dict showing TDs corresponding to the labels \u0026 convert all words\n",
        "  # to lower case in order to eliminate case sensitive issues\n",
        "  labels_dict = dict([(i['id'], i['name'].lower()) for i in objects_dictionaries])\n",
        "\n",
        "  # correcting grammatical errors\n",
        "  data_processing = spelling_correction(data_processing, m[3])\n",
        "  print_dict_characteristics(1, data_processing)\n",
        "\n",
        "  # create a mapping table to map each label to the right label structure.\n",
        "  # find the incorrect labels.\n",
        "  mapping_dict, incorrect_labels = labeling_correction(data_processing, labels_dict, m[1])\n",
        "\n",
        "  print_incorrect_labels(incorrect_labels, data_processing, m[1])\n",
        "\n",
        "  # change the 'categories' key\n",
        "  data_processing['categories'] = objects_dictionaries\n",
        "  print_dict_characteristics(2, data_processing)\n",
        "\n",
        "  # change the 'annotation' key\n",
        "  data_processing['annotations'] = annotations_key(data_processing,  incorrect_labels, mapping_dict)\n",
        "  print_dict_characteristics(3, data_processing)\n",
        "\n",
        "  # change the 'images' key\n",
        "  data_processing['images'] = images_key(data_processing)\n",
        "\n",
        "  # remove data from the 'images' key not present in the image folder\n",
        "  data_processing = annotated_images(images_folder_path, data_processing)\n",
        "  print_dict_characteristics(4, data_processing)\n",
        "\n",
        "  # align 'images' and 'annotations' key\n",
        "  data_processing = image_annotation_key(data_processing)\n",
        "  print_dict_characteristics(5, data_processing)\n",
        "\n",
        "  # write to a new JSON file\n",
        "  with open(m[2], 'w') as opened_file:\n",
        "    opened_file.write(json.dumps(data_processing, indent=4))\n",
        "\n",
        "  print('\\nDictionary characteristics after processing of', m[2].replace('.json','') ,':')\n",
        "  print('images:',len(data_processing['images']),'categories:', len(data_processing['categories']),'annotations:',len(data_processing['annotations']))\n",
        "  print('###################################################################')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "nC6XzQYL15Ki",
        "outputId": "ea8e0a7d-79bc-4321-8a63-4855afc190e1"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "      ((filepath) =\u003e {{\n",
              "        if (!google.colab.kernel.accessAllowed) {{\n",
              "          return;\n",
              "        }}\n",
              "        google.colab.files.view(filepath);\n",
              "      }})(\"/content/plastic_type_annotation.json\")"
            ],
            "text/plain": [
              "\u003cIPython.core.display.Javascript object\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# View the final JSON file\n",
        "try:\n",
        "  files.view(m[2]) # use files.download to download the file\n",
        "except ImportError:\n",
        "  pass"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7_PnincwKTE"
      },
      "source": [
        "# Visualization of categories"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ty6WVqyywL61",
        "outputId": "df519b20-2776-4a3c-d22a-14becb58992d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-09-28 21:45:30--  https://raw.githubusercontent.com/tensorflow/models/master/official/projects/waste_identification_ml/pre_processing/config/visualization.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3268 (3.2K) [text/plain]\n",
            "Saving to: ‘visualization.py’\n",
            "\n",
            "\rvisualization.py      0%[                    ]       0  --.-KB/s               \rvisualization.py    100%[===================\u003e]   3.19K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-09-28 21:45:30 (24.5 MB/s) - ‘visualization.py’ saved [3268/3268]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# download visualization script\n",
        "!wget https://raw.githubusercontent.com/tensorflow/models/master/official/projects/waste_identification_ml/pre_processing/config/visualization.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKXJsmvgwPpA",
        "outputId": "884e658e-6e37-4ae1-9f28-18e03b55ba48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['material_type_annotation.json', 'material_form_type_annotation.json']\n"
          ]
        }
      ],
      "source": [
        "from visualization import visualize_detailed_counts_horizontally\n",
        "files = glob.glob('*annotation.json')\n",
        "print(files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "id": "UjxwUBmOwdSe",
        "outputId": "3747ac0a-b298-4d37-fb06-468776aa5a78"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "material_type_annotation.json\n",
            "material_form_type_annotation.json\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "\u003cFigure size 4000x1000 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "\u003cFigure size 4000x1000 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for file in files:\n",
        "  print(os.path.basename(file))\n",
        "  visualize_detailed_counts_horizontally(file)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "JSON_Generation_for_Training.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
